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      <a href="index.html">Simba</a>
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        <h1 class="get-sub-title"><font color="#0e90d2">S</font>patial <font color="#0e90d2">I</font>n-<font color="#0e90d2">M</font>emory <font color="#0e90d2">B</font>ig data <font color="#0e90d2">A</font>nalytics</h1>
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          Simba is a distributed in-memory spatial analytics engine based on <a href="http://spark.apache.org/">Apache Spark</a>.
          It extends the Spark SQL engine across the system stack to support <b>rich spatial queries and analytics</b> through both <b>SQL</b> and
          <b>DataFrame</b> query interfaces. Besides, Simba introduces <b>native indexing support over RDDs</b> in order to develop efficient spatial operators.
          It also extends Spark SQL's query optimizer with <b>spatial-aware and cost-based optimizations</b> to make the best use of
          existing indexes and statistics.
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      <h2 class="detail-h2">Core Features</h2>

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            SQL &amp; DataFrame API
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            Simba extends the SQL and DatFrame query interfaces of Spark SQL,
            providing a natural way to express complex spatial analysis queries.
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             Index over RDDs
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            Simba supports building native (spatial) indexes over RDDs inside the kernel to achieve superior
            query performance over large data sets.
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            Efficient Algorithms
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            Simba implements efficient algorithms for different spatial operators, which are
            tailored to its indexing support and underlying Spark engine.
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            Query Optimizations
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            Simba introduces spatial and index-aware optimizations to both logical and physical optimizers of Spark SQL,
            and utilizes a CBO module to select good query plans.
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  <h1><a name="publication">Publications</a></h1>
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    <th>Venue</th>
    <th>Publication</th>
    <th>Link</th>
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    <td><a href="http://www.vldb.org/2017/">VLDB 2017</a></td>
    <td><span class="text-bold">Distributed Trajectory Similarity Search</span><br/>
        Dong Xie, Feifei Li, Jeff M. Philips
    </td>
    <td><a href="http://www.cs.utah.edu/~dongx/paper/traj-sim-vldb.pdf">pdf</a></td>
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    <td><a href="http://sigmod2016.org/">SIGMOD 2016</a></td>
    <td><span class="text-bold">Simba: Efficient In-Memory Spatial Analytics</span><br/>
        Dong Xie, Feifei Li, Bin Yao, Gefei Li, Liang Zhou, Minyi Guo
    </td>
    <td><a href="http://www.cs.utah.edu/~dongx/paper/simba-sigmod.pdf">pdf</a></td>
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    <td><a href="http://sigspatial2016.sigspatial.org">SIGSPATIAL 2016</a></td>
    <td><span class="text-bold">Simba: Spatial In-Memory Big Data Analysis (Demo Paper)</span><br/>
        Dong Xie, Feifei Li, Bin Yao, Gefei Li, Zhongpu Chen, Liang Zhou, Minyi Guo
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    <td><a href="http://www.cs.utah.edu/~dongx/paper/simba-demo.pdf">pdf</a></td>
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        <li><a href="http://www.cs.utah.edu/~dongx/">Dong Xie</a>: dongx [at] cs [dot] utah [dot] edu</li>
        <li>Gefei Li</li>
        <li>Liang Zhou</li>
		<li>Zhongpu Chen</li>
        <li><a href="http://www.cs.utah.edu/~lifeifei/">Feifei Li</li>
        <li><a href="http://www.cs.sjtu.edu.cn/~yaobin/">Bin Yao</a></li>
		<li>Minyi Guo</li>
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  <h1><a name="faq">Simba FAQ</a></h1>
  <h2>How does Simba relate to Spark SQL?</h2>
    <p class="text-small">Simba builds on Spark SQL and is optimized specially for large scale spatial
    queries and analytics over multi-dimensional data sets.</p>
  <h2>How does Simba relate to Spark SQL?</h2>
    <p class="text-small">Simba builds on Spark SQL and is optimized specially for large scale spatial
      queries and analytics over multi-dimensional data sets.</p>
  <h2>How does Simba relate to Spark SQL?</h2>
      <p class="text-small">Simba builds on Spark SQL and is optimized specially for large scale spatial
        queries and analytics over multi-dimensional data sets.</p>
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